We model centralized school matching as a second stage of a simple Tiebout-model and show that the two most discussed mechanisms, the deferred acceptance and the Boston algorithm, both produce inefficient outcomes and that the Boston mechanism is more efficient than deferred acceptance. This advantage vanishes if the participants get to know their priorities before they submit their preferences. Moreover, the mechanism creates artificial social segregation at the cost of the disadvantaged if the school priorities are based on ex ante known (social) differences of the applicants.

Abstract
We revisit the school choice problem with consent proposed by Kesten [12], which seeks to improve the efficiency of the student-optimal deferred acceptance algorithm (DA) by obtaining students' consent to give up their priorities. We observe that for students to consent, we should use their consent only when their assignments are Pareto unimprovable. Inspired by this perspective, we propose a new algorithm which iteratively reruns DA after removing students who have been matched with underdemanded schools, together with their assignments. While this algorithm is outcome equivalent to Kesten's EADAM, it is more accessible to practitioners due to its computational simplicity and transparency on consenting incentives. We also adapt this algorithm for school choice problems with weak priorities to simplify the stable improvement cycles algorithm proposed by Erdil and Ergin [8].

Abstract
Barter exchange markets are markets in which agents seek to directly trade their goods with each other. Exchanges occur in cycles or in chains in which each agent gives a good to the next agent. Kidney exchange is an important type of barter exchange market that allows incompatible patient–donor pairs to exchange kidneys so the involved patients can receive a transplant. The clearing problem is to find an allocation of donors to patients that is optimal with respect to multiple criteria. To achieve the best possible score on all criteria, long cycles and chains are often needed, particularly when there are many hard-to-match patients. In this paper we show why this may pose difficulties for existing approaches to the optimization of kidney exchanges. We then present a generic iterative branch-and-price algorithm that can deal effectively with multiple criteria, and we show how the pricing problem may be solved in polynomial time for a general class of criteria. Our algorithm is effective even for large, realistic patient–donor pools. Our approach and its effects are demonstrated by using simulations with kidney exchange data from the Netherlands and the United States.